MERS

Purpose

This is an exercise from the first module of the 2018 IDEAS Data Clinic, specifically the data visualization topic. This workshop was taught by John Drake and Ana Bento. This is an example of how to create an RMarkdown file using the MERS data.

 

---

 

mers <- read.csv("cases.csv") #load data
head(mers) #inspect the data

#clean up data by correcting errors 
mers$hospitalized[890] <- c("2015-02-20") 
mers$mers[-471,] 

## NULL

#install.packages("lubridate")
library(lubridate) #this will allow R to recognize dates

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

mers$onset2 <- ymd(mers$onset)
mers$hospitalized2 <- ymd(mers$hospitalized) #5 failed to parse
class(mers$onset2)

## [1] "Date"

#days elapsed since the start of the epidemic
day0 <- min(na.omit(mers$onset2)) #use na.omit in order to remove all NAs. If we kept them, R would not know what the minumum is (zero?)

mers$epi.day <- as.numeric(mers$onset2 - day0) #as.numeric allows us to do basic arithmetic to the data. Makes the output a number rather than a factor, for example.

---

Plot of MERS data

You can also embed plots, for example:

library(ggplot2)
ggplot(data=mers) + 
  geom_bar(mapping=aes(x=epi.day , fill=country)) + 
  labs(x="Epidemic day", y="Case count", 
       title="Global count of Mers cases by date of symptom onset",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

# if we use position="fill" this makes all of the bars have the same height (out of 1). This lets you see what countries had a case on a certain epidemic day. In our case, this isn't very helpful because we have too many countries and a large range for our x-axis. 

ggplot(data=mers) + 
  geom_bar(mapping=aes(x=epi.day , fill=country), position = "fill") + 
  labs(x="Epidemic day", y="Case count", 
       title="Global count of Mers cases by date of symptom onset",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

 

Interactive Plotly Graph

This is an interactive version of the previous plot.

 

#load packages
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

plot <- plot_ly(mers, x=~epi.day, color = ~country)
plot

## No trace type specified:
##   Based on info supplied, a 'histogram' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#histogram

 

---

Univariate Plot

mers$infectious.period <- mers$hospitalized2 - mers$onset2 #calculate "raw" infectious period
class(mers$infectious.period) #these data are class "difftime"

## [1] "difftime"

mers$infectious.period <- as.numeric(mers$infectious.period, units="days") #convert to days
ggplot(data=mers)+
  geom_histogram(aes(x=infectious.period)) +
  labs(x="Infectious period" , y="Frequency", title = "Distribution of calculated MERS infectious period",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#have negative values, so let's remove those and only keep zeros and positive values

mers$infectious.period2 <- ifelse(mers$infectious.period<0,0,mers$infectious.period)
ggplot(data=mers) + 
  geom_histogram(aes(x=infectious.period2)) +
  labs(x="Infectious period", y="Frequency", 
       title="Distribution of calculated MERS infectious period (positive values only)",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

 

---

Density Plot

ggplot(data=mers) +
  geom_density(mapping=aes(x=infectious.period2)) +
  labs(x="Infectious period", y="Frequency",
       title="Probability density for MERS infectious period (positive values only)",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

 

---

Area Plot

ggplot(data=mers) +
  geom_area(stat="bin", mapping=aes(x=infectious.period2)) +
  labs(x="Infectious period", y="Frequency",
       title="Area plot for MERS infectious period (positive values only)",
       caption="Data from: https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

 

---

Bivariate Plots

#smooth fit overall 
ggplot(data=mers) +
  geom_bar(mapping=aes(x=epi.day)) +
  geom_smooth(mapping = aes(x=epi.day, y=infectious.period2), method ="loess") +
  labs(x="Epidemic day", y="Infectious period", 
       title="Bar plot looking at the change in the infectious period \n over the course of the MERS epidemic",
       caption="Data from:https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv") 

##smooth fit per country
ggplot(data=mers) +
  geom_bar(mapping=aes(x=epi.day)) +
  geom_smooth(mapping = aes(x=epi.day, y=infectious.period2, fill=country), method ="loess") +
  labs(x="Epidemic day", y="Infectious period", 
       title="Bar plot looking at the change in the infectious period \n over the course of the MERS epidemic",
       caption="Data from:https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv") 

 

---

Faceting

This allows for multiple plots in one plot.

#plot by country
ggplot(data=mers, mapping=aes(x=epi.day, y=infectious.period2)) +
  geom_point(mapping = aes(color=country))+
  facet_wrap(~ country) +
  scale_y_continuous(limits = c(0,50)) +
  labs(x="Epidemic day", y="Infectious period",
       title="MERS infectious period (positive values only) over time",
       caption="Data from:https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv") 

## plot by gender and country
ggplot(data=subset(mers, gender %in% c("M", "F") & country %in% c("KSA", "Oman", "Iran", "Jordan", "Qatar", "South Korea", "UAE")), 
       mapping=aes(x=epi.day, y=infectious.period2)) +
  geom_point(mapping = aes(color=country))+
  facet_grid(gender ~ country) +
  scale_y_continuous(limits = c(0,50)) +
  labs(x="Epidemic day", y="Infectious period",
       title="MERS infectious period by gender and country",
       caption="Data from:https://github.com/rambaut/MERS-Cases/blob/gh-pages/data/cases.csv") 

 

---

Variation in case fatality

#create new data frame that just includes variables that are a measure of case fatality
fatality <- data.frame("Outcome"=mers$outcome, "Clinical"=mers$clinical, "Death"=mers$death)

#Create df with only fatality included. 
fat2<-fatality[fatality$Clinical=="fatal" & fatality$Outcome=="fatal",] 
head(fat2) #inspect the data